python/FunASR-XL.git

			@@ -32,8 +32,6 @@
			import yaml
			from funasr.models.base_model import FunASRModel
			from torch.utils.data import DataLoader
			from typeguard import check_argument_types
			from typeguard import check_return_type

			from funasr import __version__
			from funasr.datasets.dataset import AbsDataset
			@@ -266,9 +264,9 @@
			def build_model(cls, args: argparse.Namespace) -> FunASRModel:
			raise NotImplementedError


			@classmethod
			def get_parser(cls, parser) -> config_argparse.ArgumentParser:
			assert check_argument_types()
			def get_parser(cls) -> config_argparse.ArgumentParser:

			class ArgumentDefaultsRawTextHelpFormatter(
			argparse.RawTextHelpFormatter,
			@@ -276,10 +274,10 @@
			):
			pass

			# parser = config_argparse.ArgumentParser(
			# description="base parser",
			# formatter_class=ArgumentDefaultsRawTextHelpFormatter,
			# )
			parser = config_argparse.ArgumentParser(
			description="base parser",
			formatter_class=ArgumentDefaultsRawTextHelpFormatter,
			)

			# NOTE(kamo): Use '_' instead of '-' to avoid confusion.
			# I think '-' looks really confusing if it's written in yaml.
			@@ -958,704 +956,7 @@
			cls.trainer.add_arguments(parser)
			cls.add_task_arguments(parser)

			assert check_return_type(parser)
			return parser


			# @classmethod
			# def get_parser(cls) -> config_argparse.ArgumentParser:
			# assert check_argument_types()
			#
			# class ArgumentDefaultsRawTextHelpFormatter(
			# argparse.RawTextHelpFormatter,
			# argparse.ArgumentDefaultsHelpFormatter,
			# ):
			# pass
			#
			# parser = config_argparse.ArgumentParser(
			# description="base parser",
			# formatter_class=ArgumentDefaultsRawTextHelpFormatter,
			# )
			#
			# # NOTE(kamo): Use '_' instead of '-' to avoid confusion.
			# # I think '-' looks really confusing if it's written in yaml.
			#
			# # NOTE(kamo): add_arguments(..., required=True) can't be used
			# # to provide --print_config mode. Instead of it, do as
			# # parser.set_defaults(required=["output_dir"])
			#
			# group = parser.add_argument_group("Common configuration")
			#
			# group.add_argument(
			# "--print_config",
			# action="store_true",
			# help="Print the config file and exit",
			# )
			# group.add_argument(
			# "--log_level",
			# type=lambda x: x.upper(),
			# default="INFO",
			# choices=("ERROR", "WARNING", "INFO", "DEBUG", "NOTSET"),
			# help="The verbose level of logging",
			# )
			# group.add_argument(
			# "--dry_run",
			# type=str2bool,
			# default=False,
			# help="Perform process without training",
			# )
			# group.add_argument(
			# "--iterator_type",
			# type=str,
			# choices=["sequence", "chunk", "task", "none"],
			# default="sequence",
			# help="Specify iterator type",
			# )
			#
			# group.add_argument("--output_dir", type=str_or_none, default=None)
			# group.add_argument(
			# "--ngpu",
			# type=int,
			# default=0,
			# help="The number of gpus. 0 indicates CPU mode",
			# )
			# group.add_argument("--seed", type=int, default=0, help="Random seed")
			# group.add_argument(
			# "--num_workers",
			# type=int,
			# default=1,
			# help="The number of workers used for DataLoader",
			# )
			# group.add_argument(
			# "--num_att_plot",
			# type=int,
			# default=3,
			# help="The number images to plot the outputs from attention. "
			# "This option makes sense only when attention-based model. "
			# "We can also disable the attention plot by setting it 0",
			# )
			#
			# group = parser.add_argument_group("distributed training related")
			# group.add_argument(
			# "--dist_backend",
			# default="nccl",
			# type=str,
			# help="distributed backend",
			# )
			# group.add_argument(
			# "--dist_init_method",
			# type=str,
			# default="env://",
			# help='if init_method="env://", env values of "MASTER_PORT", "MASTER_ADDR", '
			# '"WORLD_SIZE", and "RANK" are referred.',
			# )
			# group.add_argument(
			# "--dist_world_size",
			# default=None,
			# type=int_or_none,
			# help="number of nodes for distributed training",
			# )
			# group.add_argument(
			# "--dist_rank",
			# type=int_or_none,
			# default=None,
			# help="node rank for distributed training",
			# )
			# group.add_argument(
			# # Not starting with "dist_" for compatibility to launch.py
			# "--local_rank",
			# type=int_or_none,
			# default=None,
			# help="local rank for distributed training. This option is used if "
			# "--multiprocessing_distributed=false",
			# )
			# group.add_argument(
			# "--dist_master_addr",
			# default=None,
			# type=str_or_none,
			# help="The master address for distributed training. "
			# "This value is used when dist_init_method == 'env://'",
			# )
			# group.add_argument(
			# "--dist_master_port",
			# default=None,
			# type=int_or_none,
			# help="The master port for distributed training"
			# "This value is used when dist_init_method == 'env://'",
			# )
			# group.add_argument(
			# "--dist_launcher",
			# default=None,
			# type=str_or_none,
			# choices=["slurm", "mpi", None],
			# help="The launcher type for distributed training",
			# )
			# group.add_argument(
			# "--multiprocessing_distributed",
			# default=False,
			# type=str2bool,
			# help="Use multi-processing distributed training to launch "
			# "N processes per node, which has N GPUs. This is the "
			# "fastest way to use PyTorch for either single node or "
			# "multi node data parallel training",
			# )
			# group.add_argument(
			# "--unused_parameters",
			# type=str2bool,
			# default=False,
			# help="Whether to use the find_unused_parameters in "
			# "torch.nn.parallel.DistributedDataParallel ",
			# )
			# group.add_argument(
			# "--sharded_ddp",
			# default=False,
			# type=str2bool,
			# help="Enable sharded training provided by fairscale",
			# )
			#
			# group = parser.add_argument_group("cudnn mode related")
			# group.add_argument(
			# "--cudnn_enabled",
			# type=str2bool,
			# default=torch.backends.cudnn.enabled,
			# help="Enable CUDNN",
			# )
			# group.add_argument(
			# "--cudnn_benchmark",
			# type=str2bool,
			# default=torch.backends.cudnn.benchmark,
			# help="Enable cudnn-benchmark mode",
			# )
			# group.add_argument(
			# "--cudnn_deterministic",
			# type=str2bool,
			# default=True,
			# help="Enable cudnn-deterministic mode",
			# )
			#
			# group = parser.add_argument_group("collect stats mode related")
			# group.add_argument(
			# "--collect_stats",
			# type=str2bool,
			# default=False,
			# help='Perform on "collect stats" mode',
			# )
			# group.add_argument(
			# "--mc",
			# type=bool,
			# default=False,
			# help="MultiChannel input",
			# )
			# group.add_argument(
			# "--write_collected_feats",
			# type=str2bool,
			# default=False,
			# help='Write the output features from the model when "collect stats" mode',
			# )
			#
			# group = parser.add_argument_group("Trainer related")
			# group.add_argument(
			# "--max_epoch",
			# type=int,
			# default=40,
			# help="The maximum number epoch to train",
			# )
			# group.add_argument(
			# "--max_update",
			# type=int,
			# default=sys.maxsize,
			# help="The maximum number update step to train",
			# )
			# parser.add_argument(
			# "--batch_interval",
			# type=int,
			# default=-1,
			# help="The batch interval for saving model.",
			# )
			# group.add_argument(
			# "--patience",
			# type=int_or_none,
			# default=None,
			# help="Number of epochs to wait without improvement "
			# "before stopping the training",
			# )
			# group.add_argument(
			# "--val_scheduler_criterion",
			# type=str,
			# nargs=2,
			# default=("valid", "loss"),
			# help="The criterion used for the value given to the lr scheduler. "
			# 'Give a pair referring the phase, "train" or "valid",'
			# 'and the criterion name. The mode specifying "min" or "max" can '
			# "be changed by --scheduler_conf",
			# )
			# group.add_argument(
			# "--early_stopping_criterion",
			# type=str,
			# nargs=3,
			# default=("valid", "loss", "min"),
			# help="The criterion used for judging of early stopping. "
			# 'Give a pair referring the phase, "train" or "valid",'
			# 'the criterion name and the mode, "min" or "max", e.g. "acc,max".',
			# )
			# group.add_argument(
			# "--best_model_criterion",
			# type=str2triple_str,
			# nargs="+",
			# default=[
			# ("train", "loss", "min"),
			# ("valid", "loss", "min"),
			# ("train", "acc", "max"),
			# ("valid", "acc", "max"),
			# ],
			# help="The criterion used for judging of the best model. "
			# 'Give a pair referring the phase, "train" or "valid",'
			# 'the criterion name, and the mode, "min" or "max", e.g. "acc,max".',
			# )
			# group.add_argument(
			# "--keep_nbest_models",
			# type=int,
			# nargs="+",
			# default=[10],
			# help="Remove previous snapshots excluding the n-best scored epochs",
			# )
			# group.add_argument(
			# "--nbest_averaging_interval",
			# type=int,
			# default=0,
			# help="The epoch interval to apply model averaging and save nbest models",
			# )
			# group.add_argument(
			# "--grad_clip",
			# type=float,
			# default=5.0,
			# help="Gradient norm threshold to clip",
			# )
			# group.add_argument(
			# "--grad_clip_type",
			# type=float,
			# default=2.0,
			# help="The type of the used p-norm for gradient clip. Can be inf",
			# )
			# group.add_argument(
			# "--grad_noise",
			# type=str2bool,
			# default=False,
			# help="The flag to switch to use noise injection to "
			# "gradients during training",
			# )
			# group.add_argument(
			# "--accum_grad",
			# type=int,
			# default=1,
			# help="The number of gradient accumulation",
			# )
			# group.add_argument(
			# "--bias_grad_times",
			# type=float,
			# default=1.0,
			# help="To scale the gradient of contextual related params",
			# )
			# group.add_argument(
			# "--no_forward_run",
			# type=str2bool,
			# default=False,
			# help="Just only iterating data loading without "
			# "model forwarding and training",
			# )
			# group.add_argument(
			# "--resume",
			# type=str2bool,
			# default=False,
			# help="Enable resuming if checkpoint is existing",
			# )
			# group.add_argument(
			# "--train_dtype",
			# default="float32",
			# choices=["float16", "float32", "float64"],
			# help="Data type for training.",
			# )
			# group.add_argument(
			# "--use_amp",
			# type=str2bool,
			# default=False,
			# help="Enable Automatic Mixed Precision. This feature requires pytorch>=1.6",
			# )
			# group.add_argument(
			# "--log_interval",
			# type=int_or_none,
			# default=None,
			# help="Show the logs every the number iterations in each epochs at the "
			# "training phase. If None is given, it is decided according the number "
			# "of training samples automatically .",
			# )
			# group.add_argument(
			# "--use_tensorboard",
			# type=str2bool,
			# default=True,
			# help="Enable tensorboard logging",
			# )
			# group.add_argument(
			# "--use_wandb",
			# type=str2bool,
			# default=False,
			# help="Enable wandb logging",
			# )
			# group.add_argument(
			# "--wandb_project",
			# type=str,
			# default=None,
			# help="Specify wandb project",
			# )
			# group.add_argument(
			# "--wandb_id",
			# type=str,
			# default=None,
			# help="Specify wandb id",
			# )
			# group.add_argument(
			# "--wandb_entity",
			# type=str,
			# default=None,
			# help="Specify wandb entity",
			# )
			# group.add_argument(
			# "--wandb_name",
			# type=str,
			# default=None,
			# help="Specify wandb run name",
			# )
			# group.add_argument(
			# "--wandb_model_log_interval",
			# type=int,
			# default=-1,
			# help="Set the model log period",
			# )
			# group.add_argument(
			# "--detect_anomaly",
			# type=str2bool,
			# default=False,
			# help="Set torch.autograd.set_detect_anomaly",
			# )
			#
			# group = parser.add_argument_group("Pretraining model related")
			# group.add_argument("--pretrain_path", help="This option is obsoleted")
			# group.add_argument(
			# "--init_param",
			# type=str,
			# action="append",
			# default=[],
			# help="Specify the file path used for initialization of parameters. "
			# "The format is '<file_path>:<src_key>:<dst_key>:<exclude_keys>', "
			# "where file_path is the model file path, "
			# "src_key specifies the key of model states to be used in the model file, "
			# "dst_key specifies the attribute of the model to be initialized, "
			# "and exclude_keys excludes keys of model states for the initialization."
			# "e.g.\n"
			# " # Load all parameters"
			# " --init_param some/where/model.pb\n"
			# " # Load only decoder parameters"
			# " --init_param some/where/model.pb:decoder:decoder\n"
			# " # Load only decoder parameters excluding decoder.embed"
			# " --init_param some/where/model.pb:decoder:decoder:decoder.embed\n"
			# " --init_param some/where/model.pb:decoder:decoder:decoder.embed\n",
			# )
			# group.add_argument(
			# "--ignore_init_mismatch",
			# type=str2bool,
			# default=False,
			# help="Ignore size mismatch when loading pre-trained model",
			# )
			# group.add_argument(
			# "--freeze_param",
			# type=str,
			# default=[],
			# action="append",
			# help="Freeze parameters",
			# )
			#
			# group = parser.add_argument_group("BatchSampler related")
			# group.add_argument(
			# "--num_iters_per_epoch",
			# type=int_or_none,
			# default=None,
			# help="Restrict the number of iterations for training per epoch",
			# )
			# group.add_argument(
			# "--batch_size",
			# type=int,
			# default=20,
			# help="The mini-batch size used for training. Used if batch_type='unsorted',"
			# " 'sorted', or 'folded'.",
			# )
			# group.add_argument(
			# "--valid_batch_size",
			# type=int_or_none,
			# default=None,
			# help="If not given, the value of --batch_size is used",
			# )
			# group.add_argument(
			# "--batch_bins",
			# type=int,
			# default=1000000,
			# help="The number of batch bins. Used if batch_type='length' or 'numel'",
			# )
			# group.add_argument(
			# "--valid_batch_bins",
			# type=int_or_none,
			# default=None,
			# help="If not given, the value of --batch_bins is used",
			# )
			#
			# group.add_argument("--train_shape_file", type=str, action="append", default=[])
			# group.add_argument("--valid_shape_file", type=str, action="append", default=[])
			#
			# group = parser.add_argument_group("Sequence iterator related")
			# _batch_type_help = ""
			# for key, value in BATCH_TYPES.items():
			# _batch_type_help += f'"{key}":\n{value}\n'
			# group.add_argument(
			# "--batch_type",
			# type=str,
			# default="length",
			# choices=list(BATCH_TYPES),
			# help=_batch_type_help,
			# )
			# group.add_argument(
			# "--valid_batch_type",
			# type=str_or_none,
			# default=None,
			# choices=list(BATCH_TYPES) + [None],
			# help="If not given, the value of --batch_type is used",
			# )
			# group.add_argument(
			# "--speech_length_min",
			# type=int,
			# default=-1,
			# help="speech length min",
			# )
			# group.add_argument(
			# "--speech_length_max",
			# type=int,
			# default=-1,
			# help="speech length max",
			# )
			# group.add_argument("--fold_length", type=int, action="append", default=[])
			# group.add_argument(
			# "--sort_in_batch",
			# type=str,
			# default="descending",
			# choices=["descending", "ascending"],
			# help="Sort the samples in each mini-batches by the sample "
			# 'lengths. To enable this, "shape_file" must have the length information.',
			# )
			# group.add_argument(
			# "--sort_batch",
			# type=str,
			# default="descending",
			# choices=["descending", "ascending"],
			# help="Sort mini-batches by the sample lengths",
			# )
			# group.add_argument(
			# "--multiple_iterator",
			# type=str2bool,
			# default=False,
			# help="Use multiple iterator mode",
			# )
			#
			# group = parser.add_argument_group("Chunk iterator related")
			# group.add_argument(
			# "--chunk_length",
			# type=str_or_int,
			# default=500,
			# help="Specify chunk length. e.g. '300', '300,400,500', or '300-400'."
			# "If multiple numbers separated by command are given, "
			# "one of them is selected randomly for each samples. "
			# "If two numbers are given with '-', it indicates the range of the choices. "
			# "Note that if the sequence length is shorter than the all chunk_lengths, "
			# "the sample is discarded. ",
			# )
			# group.add_argument(
			# "--chunk_shift_ratio",
			# type=float,
			# default=0.5,
			# help="Specify the shift width of chunks. If it's less than 1, "
			# "allows the overlapping and if bigger than 1, there are some gaps "
			# "between each chunk.",
			# )
			# group.add_argument(
			# "--num_cache_chunks",
			# type=int,
			# default=1024,
			# help="Shuffle in the specified number of chunks and generate mini-batches "
			# "More larger this value, more randomness can be obtained.",
			# )
			#
			# group = parser.add_argument_group("Dataset related")
			# _data_path_and_name_and_type_help = (
			# "Give three words splitted by comma. It's used for the training data. "
			# "e.g. '--train_data_path_and_name_and_type some/path/a.scp,foo,sound'. "
			# "The first value, some/path/a.scp, indicates the file path, "
			# "and the second, foo, is the key name used for the mini-batch data, "
			# "and the last, sound, decides the file type. "
			# "This option is repeatable, so you can input any number of features "
			# "for your task. Supported file types are as follows:\n\n"
			# )
			# for key, dic in DATA_TYPES.items():
			# _data_path_and_name_and_type_help += f'"{key}":\n{dic["help"]}\n\n'
			#
			# # for large dataset
			# group.add_argument(
			# "--dataset_type",
			# type=str,
			# default="small",
			# help="whether to use dataloader for large dataset",
			# )
			# parser.add_argument(
			# "--dataset_conf",
			# action=NestedDictAction,
			# default=dict(),
			# help=f"The keyword arguments for dataset",
			# )
			# group.add_argument(
			# "--train_data_file",
			# type=str,
			# default=None,
			# help="train_list for large dataset",
			# )
			# group.add_argument(
			# "--valid_data_file",
			# type=str,
			# default=None,
			# help="valid_list for large dataset",
			# )
			#
			# group.add_argument(
			# "--train_data_path_and_name_and_type",
			# type=str2triple_str,
			# action="append",
			# default=[],
			# help=_data_path_and_name_and_type_help,
			# )
			# group.add_argument(
			# "--valid_data_path_and_name_and_type",
			# type=str2triple_str,
			# action="append",
			# default=[],
			# )
			# group.add_argument(
			# "--allow_variable_data_keys",
			# type=str2bool,
			# default=False,
			# help="Allow the arbitrary keys for mini-batch with ignoring "
			# "the task requirements",
			# )
			# group.add_argument(
			# "--max_cache_size",
			# type=humanfriendly.parse_size,
			# default=0.0,
			# help="The maximum cache size for data loader. e.g. 10MB, 20GB.",
			# )
			# group.add_argument(
			# "--max_cache_fd",
			# type=int,
			# default=32,
			# help="The maximum number of file descriptors to be kept "
			# "as opened for ark files. "
			# "This feature is only valid when data type is 'kaldi_ark'.",
			# )
			# group.add_argument(
			# "--valid_max_cache_size",
			# type=humanfriendly_parse_size_or_none,
			# default=None,
			# help="The maximum cache size for validation data loader. e.g. 10MB, 20GB. "
			# "If None, the 5 percent size of --max_cache_size",
			# )
			#
			# group = parser.add_argument_group("Optimizer related")
			# for i in range(1, cls.num_optimizers + 1):
			# suf = "" if i == 1 else str(i)
			# group.add_argument(
			# f"--optim{suf}",
			# type=lambda x: x.lower(),
			# default="adadelta",
			# choices=list(optim_classes),
			# help="The optimizer type",
			# )
			# group.add_argument(
			# f"--optim{suf}_conf",
			# action=NestedDictAction,
			# default=dict(),
			# help="The keyword arguments for optimizer",
			# )
			# group.add_argument(
			# f"--scheduler{suf}",
			# type=lambda x: str_or_none(x.lower()),
			# default=None,
			# choices=list(scheduler_classes) + [None],
			# help="The lr scheduler type",
			# )
			# group.add_argument(
			# f"--scheduler{suf}_conf",
			# action=NestedDictAction,
			# default=dict(),
			# help="The keyword arguments for lr scheduler",
			# )
			#
			# # for training on PAI
			# group = parser.add_argument_group("PAI training related")
			# group.add_argument(
			# "--use_pai",
			# type=str2bool,
			# default=False,
			# help="flag to indicate whether training on PAI",
			# )
			# group.add_argument(
			# "--simple_ddp",
			# type=str2bool,
			# default=False,
			# )
			# group.add_argument(
			# "--num_worker_count",
			# type=int,
			# default=1,
			# help="The number of machines on PAI.",
			# )
			# group.add_argument(
			# "--access_key_id",
			# type=str,
			# default=None,
			# help="The username for oss.",
			# )
			# group.add_argument(
			# "--access_key_secret",
			# type=str,
			# default=None,
			# help="The password for oss.",
			# )
			# group.add_argument(
			# "--endpoint",
			# type=str,
			# default=None,
			# help="The endpoint for oss.",
			# )
			# group.add_argument(
			# "--bucket_name",
			# type=str,
			# default=None,
			# help="The bucket name for oss.",
			# )
			# group.add_argument(
			# "--oss_bucket",
			# default=None,
			# help="oss bucket.",
			# )
			#
			# cls.trainer.add_arguments(parser)
			# cls.add_task_arguments(parser)
			#
			# assert check_return_type(parser)
			# return parser

			@classmethod
			def build_optimizers(
			@@ -1702,7 +1003,6 @@
			return _cls

			# This method is used only for --print_config
			assert check_argument_types()
			parser = cls.get_parser()
			args, _ = parser.parse_known_args()
			config = vars(args)
			@@ -1742,7 +1042,6 @@

			@classmethod
			def check_required_command_args(cls, args: argparse.Namespace):
			assert check_argument_types()
			if hasattr(args, "required"):
			for k in vars(args):
			if "-" in k:
			@@ -1772,7 +1071,6 @@
			inference: bool = False,
			) -> None:
			"""Check if the dataset satisfy the requirement of current Task"""
			assert check_argument_types()
			mes = (
			f"If you intend to use an additional input, modify "
			f'"{cls.__name__}.required_data_names()" or '
			@@ -1799,14 +1097,12 @@

			@classmethod
			def print_config(cls, file=sys.stdout) -> None:
			assert check_argument_types()
			# Shows the config: e.g. python train.py asr --print_config
			config = cls.get_default_config()
			file.write(yaml_no_alias_safe_dump(config, indent=4, sort_keys=False))

			@classmethod
			def main(cls, args: argparse.Namespace = None, cmd: Sequence[str] = None):
			assert check_argument_types()
			print(get_commandline_args(), file=sys.stderr)
			if args is None:
			parser = cls.get_parser()
			@@ -1843,7 +1139,6 @@

			@classmethod
			def main_worker(cls, args: argparse.Namespace):
			assert check_argument_types()

			# 0. Init distributed process
			distributed_option = build_dataclass(DistributedOption, args)
			@@ -2071,25 +1366,10 @@

			# 7. Build iterator factories
			if args.dataset_type == "large":
			from funasr.datasets.large_datasets.build_dataloader import ArkDataLoader
			train_iter_factory = ArkDataLoader(args.train_data_file, args.token_list, args.dataset_conf,
			frontend_conf=args.frontend_conf if hasattr(args,
			"frontend_conf") else None,
			seg_dict_file=args.seg_dict_file if hasattr(args,
			"seg_dict_file") else None,
			punc_dict_file=args.punc_list if hasattr(args,
			"punc_list") else None,
			bpemodel_file=args.bpemodel if hasattr(args, "bpemodel") else None,
			mode="train")
			valid_iter_factory = ArkDataLoader(args.valid_data_file, args.token_list, args.dataset_conf,
			frontend_conf=args.frontend_conf if hasattr(args,
			"frontend_conf") else None,
			seg_dict_file=args.seg_dict_file if hasattr(args,
			"seg_dict_file") else None,
			punc_dict_file=args.punc_list if hasattr(args,
			"punc_list") else None,
			bpemodel_file=args.bpemodel if hasattr(args, "bpemodel") else None,
			mode="eval")
			from funasr.datasets.large_datasets.build_dataloader import LargeDataLoader
			train_iter_factory = LargeDataLoader(args, mode="train")
			valid_iter_factory = LargeDataLoader(args, mode="eval")

			elif args.dataset_type == "small":
			train_iter_factory = cls.build_iter_factory(
			args=args,
			@@ -2266,7 +1546,6 @@
			- 4 epoch with "--num_iters_per_epoch" == 4

			"""
			assert check_argument_types()
			iter_options = cls.build_iter_options(args, distributed_option, mode)

			# Overwrite iter_options if any kwargs is given
			@@ -2299,7 +1578,6 @@
			def build_sequence_iter_factory(
			cls, args: argparse.Namespace, iter_options: IteratorOptions, mode: str
			) -> AbsIterFactory:
			assert check_argument_types()

			if hasattr(args, "frontend_conf"):
			if args.frontend_conf is not None and "fs" in args.frontend_conf:
			@@ -2393,7 +1671,6 @@
			iter_options: IteratorOptions,
			mode: str,
			) -> AbsIterFactory:
			assert check_argument_types()

			dataset = ESPnetDataset(
			iter_options.data_path_and_name_and_type,
			@@ -2498,7 +1775,6 @@
			def build_multiple_iter_factory(
			cls, args: argparse.Namespace, distributed_option: DistributedOption, mode: str
			):
			assert check_argument_types()
			iter_options = cls.build_iter_options(args, distributed_option, mode)
			assert len(iter_options.data_path_and_name_and_type) > 0, len(
			iter_options.data_path_and_name_and_type
			@@ -2595,7 +1871,6 @@
			inference: bool = False,
			) -> DataLoader:
			"""Build DataLoader using iterable dataset"""
			assert check_argument_types()
			# For backward compatibility for pytorch DataLoader
			if collate_fn is not None:
			kwargs = dict(collate_fn=collate_fn)
			@@ -2645,7 +1920,6 @@
			device: Device type, "cpu", "cuda", or "cuda:N".

			"""
			assert check_argument_types()
			if config_file is None:
			assert model_file is not None, (
			"The argument 'model_file' must be provided "